It is important to note that in contrast to standard applications of copula theory, we do not aim at
estimating the full data generating distribution based on (REF ). Instead, following the investigations by [1]}, our goal is to capture the tail-behavior of \({\mathbf {x}}\) . Hence, we propose to learn surrogate marginals \({\mathbf {z}}_1, \dots , {\mathbf {z}}_D\)  that are able to represent the tailedness of the marginals \({\mathbf {x}}_1, \dots ,{\mathbf {x}}_D\) . By combining these marginals with some simple copula structure, such as the Gaussian Copula or the Independence Copula (see (REF ) below), we are able to create a joint distribution that represents the marginal tail behavior of \({\mathbf {x}}\) .
